from transformers import AlbertForSequenceClassification, AlbertTokenizer, Trainer, TrainingArguments
from datasets import load_dataset

# Load a dataset (replace with your dataset)
dataset = load_dataset("text", data_files={"train": "path/to/train.txt", "test": "path/to/test.txt"})

# Preprocess the dataset (tokenization, formatting, etc.)
def preprocess_function(examples):
	return tokenizer(examples["text"], padding="max_length", truncation=True)

tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
tokenized_dataset = dataset.map(preprocess_function, batched=True)

# Load the model
model = AlbertForSequenceClassification.from_pretrained("albert-base-v2", num_labels=2) # Adjust num_labels as needed

# Define training arguments
training_args = TrainingArguments(
	output_dir="./results",
	num_train_epochs=3,
	per_device_train_batch_size=8,
	per_device_eval_batch_size=8,
	warmup_steps=500,
	weight_decay=0.01,
	evaluate_during_training=True,
	logging_dir="./logs",
)

# Initialize the Trainer
trainer = Trainer(
	model=model,
	args=training_args,
	train_dataset=tokenized_dataset["train"],
	eval_dataset=tokenized_dataset["test"]
)

# Train the model
trainer.train()

# Save the fine-tuned model
model.save_pretrained("path/to/save/model")